Frontmatter
Bibliographic record
Abstract
Universities across North America and beyond are experiencing growing demand for off-campus experiential learning.Exploring the foundations of what it means to learn "out there," Out There Learning is an informed critical investigation of the pedagogical philosophies and practices involved in short-term off-campus programs or field courses.Bringing together contributors' individual research and experience teaching or administering these programs, Out There Learning examines and challenges common assumptions about pedagogy, place, and personal transformation, while also providing experience-based insights and advice for getting the most out of faculty-led field courses.Divided into three sections that investigate aspects of pedagogy, ethics of place, and course and program assessment, this collection also offers voices "from the field," highlighting the experiences of faculty members, students, teaching assistants, and community members engaged in every aspect of off-campus study programs.Several chapters examine the programs in the traditional territories of Indigenous communities and in the Global South.Containing an appendix highlighting some examples of off-campus study programs, Out There Learning offers new pathways for faculty, staff, and college and university administrators interested in enriching non-traditional avenues of study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.875 | 0.737 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".